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Record W4200370142 · doi:10.1016/j.drudis.2021.12.012

Therapeutic potential of targeting regulatory mechanisms of hepatic stellate cell activation in liver fibrosis

2021· review· en· W4200370142 on OpenAlexaff
Kaveh Baghaei, Sogol Mazhari, Samaneh Tokhanbigli, Gilda Parsamanesh, Helia Alavifard, Dedmer Schaafsma, Saeid Ghavami

Bibliographic record

VenueDrug Discovery Today · 2021
Typereview
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyChildren's Hospital Research Institute of ManitobaCancerCare ManitobaImpact
Fundersnot available
KeywordsHepatic stellate cellExtracellular matrixHepatic fibrosisFibrosisLiver fibrosisCancer researchBiologyMedicineCell biologyPathology

Abstract

fetched live from OpenAlex

Hepatic fibrosis is a manifestation of different etiologies of liver disease with the involvement of multiple mediators in complex network interactions. Activated hepatic stellate cells (aHSCs) are the central driver of hepatic fibrosis, given their potential to induce connective tissue formation and extracellular matrix (ECM) protein accumulation. Therefore, identifying the cellular and molecular pathways involved in the activation of HSCs is crucial in gaining mechanistic and therapeutic perspectives to more effectively target the disease. In addition to a comprehensive summary of our current understanding of the role of HSCs in liver fibrosis, we also discuss here the proposed therapeutic strategies based on targeting HSCs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.272
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations71
Published2021
Admission routes1
Has abstractyes

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